Visit Apps Ved →

wmo

World Model Optimizer CLI for routing, distilling, and serving models

TLDR

Install and configure providers
$ pip install world-model-optimizer
$ wmo providers set
Build a model from agent traces
$ wmo build --file [traces.jsonl] --name [my-model]
Serve a tuned router/model
$ wmo serve --name [my-model]
Log in to the hosted platform
$ wmo login
Run a hosted agent or world model
$ wmo run [agent-id]

SYNOPSIS

wmo command [options]

DESCRIPTION

wmo (World Model Optimizer) turns collected agent traces into smaller open models and cost-aware routers. It can distill via external APIs (e.g. Tinker), fit routing policies on held-out traces, and serve a local endpoint that keeps frontier quality while cutting inference cost.The project also ships world-model simulation APIs for closed-loop agent testing and optional E2B sandbox backends for harness optimization. Local state lives under `.wmo/`.

PARAMETERS

providers set

Register model providers and candidates in `.wmo/pool.toml`
build --file traces --name name
Build a world model / routing setup from OTel or agent traces
optimize
Subcommands for route sweep/fit/report, distill, harness, and pin
serve --name name
Serve an endpoint that routes between frontier and smaller models
login
Authenticate against the Experiential Labs hosted platform
run id
Run a hosted world model or agent harness
config telemetry {enable|disable|status}
Control anonymous usage telemetry
eval
Evaluate harnesses/tasks (optional E2B backend)

CAVEATS

Requires Python packaging (`pip install world-model-optimizer`) and provider API keys for most optimize/serve workflows. Hosted features need `wmo login`. Telemetry is on by default; disable with `wmo config telemetry disable` or `DONOTTRACK=1` / `WMO_TELEMETRY=0`.

HISTORY

Open-source CLI from Experiential Labs for continual improvement of agent models from production traces.

SEE ALSO

pip(1), python(1)

RESOURCES

Source code · Homepage